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Automatic Collection of Fuel Prices from a Network of Mobile Cameras

Automatic Collection of Fuel Prices from a Network of Mobile Cameras. Tristan Gibeau. Outline. Introduction History & Background System Design Computer Vision Algorithms Evaluation & Prototype Testing Future Developments Conclusion & Thoughts . Introduction. Introduction. Fuel Prices

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Automatic Collection of Fuel Prices from a Network of Mobile Cameras

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  1. Automatic Collection of Fuel Prices from a Network of Mobile Cameras Tristan Gibeau

  2. Outline • Introduction • History & Background • System Design • Computer Vision Algorithms • Evaluation & Prototype Testing • Future Developments • Conclusion & Thoughts

  3. Introduction

  4. Introduction • Fuel Prices • In the past they have been very flexible • No way to see who has the cheapest prices • What to say they will not go back up!

  5. Solution… • Keep track of current fuel prices via mobile network of cameras • Developed by • University of New South Wales, Sidney, Australia • Y.F. Dong • S. Kanhere • C.T. Chou • Portland State University, USA • N. Bulusu

  6. Focus • How do we collect the fuel prices? • Develop a system of Wireless Sensor Networks (WSN) • Key Features • Mobile Camera • Global Positioning System (GPS) • Geographic Information System (GIS) • Collect fuel price images • Road side service station billboard signs

  7. Critical Key Element • Computer Vision Algorithm • Extracting Fuel Prices from images • Segmentation • Dimensions & Histogram Comparison • Character Extraction & Classification • Necessary component of the system

  8. Foundations • Important Elements of Successful Software • Easy use of software for end user • Uploading • Sharing • Searching Data • Low cost for usage • Uploads & Downloads

  9. History & Background

  10. History & Background • Similar types ofprojects • USA • GasBuddy • Gaswatch • UK • Fuelprice • Australia • Motormouth • Fuelwatch

  11. GasBuddy, Gaswatch, & FuelPrice • Participatory Sensor Network • Workers and Volunteers update prices • Covers both US, Canada, & UK (FuelPrice) • Search by state, province, city, or address • View service stations via maps • iPhone & Smart Phone integration • Problems • Manual Collection • Data may be wrong, out of date, or just not available

  12. Motormouth & Fuelwatch • Similar to GasBuddy, Gaswatch & FuelPrice • Focus on major cities in Australia • Backed and Sponsored by the Australian Government • Keeps prices regulated • Problems • Still manual entry • Lack of some service stations not offered

  13. System Design Ideal Configuration

  14. Ideal Configuration • Two Key Features • Fuel Price Collection • User Query • Utilize a WSN with SenseMart • SenseMart uses existing infrastructure • Help cut down on cost • Hardware • Mobile Smart Phone • GPS • GIS • Remote Server • Price Detection Algorithm

  15. Fuel Price Collection • Images taken from mobile phone automatically • Triggered by proximity of service station • Utilized by GPS & GIS software running on device • When in proximity • GIS software initializes photo trigger event • Series of photos are taken • Photos are then uploaded to remote server • They are then processed by price detection algorithm • Computer Vision Algorithm • GPS & service station are uploaded as well

  16. User Query • Fuel Prices Storage • Database setup with user interface • Old fuel prices are kept for history • User Query • User initializes query via • Web page • Mobile Application • Short Message Service (SMS)

  17. Ideal System Diagram

  18. System Design Prototype & Computer Vision Algorithm

  19. Prototype • Developed First • Ensure that the critical components work • Data Gathering • Images of service station fuel price billboards • Computer Vision Algorithm • Select & Clean up image for segmentation • Segment out billboard from image • Detect fuel prices

  20. Computer Vision Algorithm • Little History • Detecting items in images is complicated • Blurry, out of focus, motion sensitive and low light • Items may appear like others • Similar color or shape • Target blind spots • Things may block the view of target • Trees, people, cars, signs, and so on… • Developing an algorithm that is perfect • IMPOSSIBLE!!!!

  21. Fuel Price Detection • Segmentation & Color Thresholding • Programmed with two sign types • Mobil & BP • Segment out the billboard color configuration • This allows the algorithm to ignore everything else • Based on Red, Green, & Blue (RGB) • Hue, Intensity, and Saturation (HIS) • Dimension & Histogram Comparison • Utilize what is known about the price area • Compare the dimensions of the fuel prices • Analyze the histogram to see if it has the same trend

  22. Step by Step of Segmentation

  23. Character Recognition • Utilizes FeedforwardBackpropagation Neural Network (FFBPNN) • Extraction of characters • Classification by a neural network • Character Extraction • Binary Image Conversion • Bounding box algorithm • Construction of feature vectors • Recognition • Trained from other sample fuel price boards

  24. Evaluation & Prototype Testing

  25. Evaluation and Prototype Testing • 52 Image Set • 3 BP Service Stations • 5 Mobil Service Stations • Imaging Devices • 5 Megapixel Nokia N95 Mobile Phone • 4 Megapixel Canon IXUS 400 Camera • Mounted by passenger in vehicle • Testing Images based on • Distance • Weather (Sunny or Cloudy) • Daylight Disparities

  26. Results • Fuel Detection Algorithm Results – Billboard • 15/52 image set were blurry or out of focus • Algorithm did not detect properly • Positive Detection: 33 Images • 15 Mobil & 18 BP • 330 Characters & 99 Fuel Prices

  27. Billboard Breakdown • Service Station Fuel Billboard Results

  28. Future Developments

  29. Future Developments • Develop and test the ideal system • Test both GPS and GIS integration • Move the image processing to the mobile phone • Help reduce the overhead between the client and server • Enhance Fuel Price Detection Algorithm • Support for more service station chains • Integrate with GIS • Such as Street View with Google Maps

  30. Conclusion & Thoughts

  31. Conclusion • Ideal • Good use of wireless sensor networks • First use of an WSN for consumer pricing information • Help make users aware of current gas prices • Make prices more easily updated • Prototype • 87.7% successful detection of fuel prices

  32. Thoughts • Adapt the system to work with current GIS • Integrate into Google Maps • Display current prices of service stations • Integrate with GPS & GIS providers • Creates more competition between service stations • May reduce the prices of fuel • Work with service stations to supply their current prices • Build infrastructure to integrate with service station computer system

  33. Questions?

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